Speech technology is not appropriately explored even though modern advances in speech
technology—especially those driven by deep learning (DL) technology—offer …

Speech-centric machine learning systems have revolutionized a number of leading
industries ranging from transportation and healthcare to education and defense …

Traditionally, speech emotion recognition (SER) research has relied on manually
handcrafted acoustic features using feature engineering. However, the design of …

Research on speech processing has traditionally considered the task of designing hand-
engineered acoustic features (feature engineering) as a separate distinct problem from the …

Speech emotion recognition (SER) as a Machine Learning (ML) problem continues to
garner a significant amount of research interest, especially in the affective computing …

Voice User Interfaces (VUIs) are increasingly popular and built into smartphones, home
assistants, and Internet of Things (IoT) devices. Despite offering an always-on convenient …

In differentially private stochastic gradient descent (DPSGD), gradient clipping and random
noise addition disproportionately affect underrepresented and complex classes and …

Multimodal learning has shown great potentials in numerous scenes and attracts increasing
interest recently. However, it often encounters the problem of missing modality data and thus …

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems
lack generalisation across different conditions. A key underlying reason for poor …

Machine Unlearning, a pivotal field addressing data privacy in machine learning,
necessitates efficient methods for the removal of private or irrelevant data. In this context …